Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations66450
Missing cells142192
Missing cells (%)9.7%
Duplicate rows1413
Duplicate rows (%)2.1%
Total size in memory10.5 MiB
Average record size in memory165.0 B

Variable types

Text4
DateTime5
Numeric7
Categorical3
Boolean2
Unsupported1

Alerts

timezone has constant value "America/Los_Angeles" Constant
paymentRequired has constant value "True" Constant
Dataset has 1413 (2.1%) duplicate rowsDuplicates
connectionWeekdayName is highly overall correlated with isWeekendHigh correlation
isWeekend is highly overall correlated with connectionWeekdayNameHigh correlation
kWhDelivered is highly overall correlated with kWhRequested and 1 other fieldsHigh correlation
kWhRequested is highly overall correlated with kWhDelivered and 1 other fieldsHigh correlation
milesRequested is highly overall correlated with kWhDelivered and 1 other fieldsHigh correlation
isWeekend is highly imbalanced (57.7%) Imbalance
doneChargingTime has 4088 (6.2%) missing values Missing
userID has 17263 (26.0%) missing values Missing
WhPerMile has 17263 (26.0%) missing values Missing
kWhRequested has 17263 (26.0%) missing values Missing
milesRequested has 17263 (26.0%) missing values Missing
minutesAvailable has 17263 (26.0%) missing values Missing
modifiedAt has 17263 (26.0%) missing values Missing
paymentRequired has 17263 (26.0%) missing values Missing
requestedDeparture has 17263 (26.0%) missing values Missing
connectionTimespan is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2024-11-14 18:43:02.280811
Analysis finished2024-11-14 18:43:06.638762
Duration4.36 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

id
Text

Distinct65037
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:06.751275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters1594800
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63624 ?
Unique (%)95.7%

Sample

1st row5e23b149f9af8b5fe4b973cf
2nd row5e23b149f9af8b5fe4b973d0
3rd row5e23b149f9af8b5fe4b973d1
4th row5e23b149f9af8b5fe4b973d2
5th row5e23b149f9af8b5fe4b973d3
ValueCountFrequency (%)
5d3b9b4ff9af8b7392318319 2
 
< 0.1%
5d4f61d6f9af8b30fe7ecfe8 2
 
< 0.1%
5d574ad2f9af8b4c10c0364a 2
 
< 0.1%
5d574ad2f9af8b4c10c03649 2
 
< 0.1%
5d574ad2f9af8b4c10c03648 2
 
< 0.1%
5d574ad2f9af8b4c10c03647 2
 
< 0.1%
5d574ad2f9af8b4c10c03646 2
 
< 0.1%
5d574ad2f9af8b4c10c03645 2
 
< 0.1%
5d574ad2f9af8b4c10c0364d 2
 
< 0.1%
5d55f950f9af8b45dfb3d8a3 2
 
< 0.1%
Other values (65027) 66430
> 99.9%
2024-11-14T19:43:06.932721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 186880
11.7%
b 134254
 
8.4%
9 132068
 
8.3%
a 125894
 
7.9%
8 123025
 
7.7%
5 116280
 
7.3%
c 92936
 
5.8%
d 89241
 
5.6%
6 88316
 
5.5%
7 79006
 
5.0%
Other values (6) 426900
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1594800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 186880
11.7%
b 134254
 
8.4%
9 132068
 
8.3%
a 125894
 
7.9%
8 123025
 
7.7%
5 116280
 
7.3%
c 92936
 
5.8%
d 89241
 
5.6%
6 88316
 
5.5%
7 79006
 
5.0%
Other values (6) 426900
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1594800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 186880
11.7%
b 134254
 
8.4%
9 132068
 
8.3%
a 125894
 
7.9%
8 123025
 
7.7%
5 116280
 
7.3%
c 92936
 
5.8%
d 89241
 
5.6%
6 88316
 
5.5%
7 79006
 
5.0%
Other values (6) 426900
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1594800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 186880
11.7%
b 134254
 
8.4%
9 132068
 
8.3%
a 125894
 
7.9%
8 123025
 
7.7%
5 116280
 
7.3%
c 92936
 
5.8%
d 89241
 
5.6%
6 88316
 
5.5%
7 79006
 
5.0%
Other values (6) 426900
26.8%
Distinct64839
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
Minimum2018-04-25 04:08:04-07:00
Maximum2021-09-13 22:43:39-07:00
2024-11-14T19:43:07.003200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:07.053133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct64906
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
Minimum2018-04-25 06:20:10-07:00
Maximum2021-09-14 07:46:28-07:00
2024-11-14T19:43:07.215328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:07.266551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

doneChargingTime
Date

Missing 

Distinct60637
Distinct (%)97.2%
Missing4088
Missing (%)6.2%
Memory size519.3 KiB
Minimum2018-04-25 06:21:10-07:00
Maximum2021-09-14 07:46:22-07:00
2024-11-14T19:43:07.314662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:07.364008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

kWhDelivered
Real number (ℝ)

High correlation 

Distinct25629
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.787916
Minimum0.501
Maximum108.79724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:07.412435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.501
5-th percentile0.917
Q15.097
median9.14
Q314.183
95-th percentile34.8814
Maximum108.79724
Range108.29624
Interquartile range (IQR)9.086

Descriptive statistics

Standard deviation10.314789
Coefficient of variation (CV)0.87503069
Kurtosis3.9074214
Mean11.787916
Median Absolute Deviation (MAD)4.657
Skewness1.8274044
Sum783307.05
Variance106.39486
MonotonicityNot monotonic
2024-11-14T19:43:07.458169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.886 31
 
< 0.1%
0.901 31
 
< 0.1%
0.878 30
 
< 0.1%
0.86 28
 
< 0.1%
0.897 28
 
< 0.1%
0.852 27
 
< 0.1%
13.893 27
 
< 0.1%
13.874 26
 
< 0.1%
0.853 26
 
< 0.1%
0.892 26
 
< 0.1%
Other values (25619) 66170
99.6%
ValueCountFrequency (%)
0.501 3
< 0.1%
0.502 5
< 0.1%
0.503 2
 
< 0.1%
0.5037679211 1
 
< 0.1%
0.505 1
 
< 0.1%
0.506 2
 
< 0.1%
0.507 2
 
< 0.1%
0.508 2
 
< 0.1%
0.5081750299 1
 
< 0.1%
0.509 1
 
< 0.1%
ValueCountFrequency (%)
108.7972417 1
< 0.1%
89.36273194 1
< 0.1%
77.7 1
< 0.1%
75.696 1
< 0.1%
75.528 1
< 0.1%
72.582 1
< 0.1%
69.74 1
< 0.1%
69.373 1
< 0.1%
68.74 1
< 0.1%
68.609 1
< 0.1%
Distinct65037
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:07.582362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length38
Mean length37.991543
Min length37

Characters and Unicode

Total characters2524538
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63624 ?
Unique (%)95.7%

Sample

1st row1_1_179_810_2020-01-02 13:08:53.870034
2nd row1_1_193_825_2020-01-02 13:36:49.599853
3rd row1_1_193_829_2020-01-02 13:56:35.214993
4th row1_1_193_820_2020-01-02 13:59:58.309319
5th row1_1_193_819_2020-01-02 14:00:00.779967
ValueCountFrequency (%)
2_39_81_4550_2021-07-06 9
 
< 0.1%
2_39_81_4550_2021-08-21 9
 
< 0.1%
2_39_81_4550_2021-08-01 9
 
< 0.1%
2_39_130_31_2018-10-28 9
 
< 0.1%
2_39_81_4550_2021-08-28 9
 
< 0.1%
2_39_81_4550_2021-09-10 8
 
< 0.1%
2_39_81_4550_2021-06-24 8
 
< 0.1%
2_39_81_4550_2021-06-19 8
 
< 0.1%
2_39_127_19_2018-07-28 8
 
< 0.1%
2_39_81_4550_2021-07-04 8
 
< 0.1%
Other values (111178) 132815
99.9%
2024-11-14T19:43:07.772689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 389675
15.4%
2 278190
11.0%
_ 265800
10.5%
0 262018
10.4%
9 188446
7.5%
3 165220
 
6.5%
8 145176
 
5.8%
- 132900
 
5.3%
: 132900
 
5.3%
7 124082
 
4.9%
Other values (5) 440131
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2524538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 389675
15.4%
2 278190
11.0%
_ 265800
10.5%
0 262018
10.4%
9 188446
7.5%
3 165220
 
6.5%
8 145176
 
5.8%
- 132900
 
5.3%
: 132900
 
5.3%
7 124082
 
4.9%
Other values (5) 440131
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2524538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 389675
15.4%
2 278190
11.0%
_ 265800
10.5%
0 262018
10.4%
9 188446
7.5%
3 165220
 
6.5%
8 145176
 
5.8%
- 132900
 
5.3%
: 132900
 
5.3%
7 124082
 
4.9%
Other values (5) 440131
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2524538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 389675
15.4%
2 278190
11.0%
_ 265800
10.5%
0 262018
10.4%
9 188446
7.5%
3 165220
 
6.5%
8 145176
 
5.8%
- 132900
 
5.3%
: 132900
 
5.3%
7 124082
 
4.9%
Other values (5) 440131
17.4%

siteID
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
1
35042 
2
31408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 35042
52.7%
2 31408
47.3%

Length

2024-11-14T19:43:07.833603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-14T19:43:07.869950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 35042
52.7%
2 31408
47.3%

Most occurring characters

ValueCountFrequency (%)
1 35042
52.7%
2 31408
47.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 35042
52.7%
2 31408
47.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 35042
52.7%
2 31408
47.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 35042
52.7%
2 31408
47.3%
Distinct107
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:07.987830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.5612039
Min length6

Characters and Unicode

Total characters435992
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAG-3F30
2nd rowAG-1F01
3rd rowAG-1F03
4th rowAG-1F04
5th rowAG-1F06
ValueCountFrequency (%)
ca-303 1805
 
2.7%
ag-1f08 1496
 
2.3%
ca-305 1411
 
2.1%
ag-1f10 1313
 
2.0%
ag-1f06 1251
 
1.9%
ag-1f04 1192
 
1.8%
ag-1f13 1170
 
1.8%
ca-307 1140
 
1.7%
11900388 1125
 
1.7%
ag-1f02 1111
 
1.7%
Other values (97) 53436
80.4%
2024-11-14T19:43:08.178959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 65325
15.0%
- 65325
15.0%
3 46944
10.8%
1 41082
9.4%
G 35042
8.0%
F 35042
8.0%
C 30283
6.9%
0 27725
6.4%
4 26056
 
6.0%
2 18603
 
4.3%
Other values (5) 44565
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 435992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 65325
15.0%
- 65325
15.0%
3 46944
10.8%
1 41082
9.4%
G 35042
8.0%
F 35042
8.0%
C 30283
6.9%
0 27725
6.4%
4 26056
 
6.0%
2 18603
 
4.3%
Other values (5) 44565
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 435992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 65325
15.0%
- 65325
15.0%
3 46944
10.8%
1 41082
9.4%
G 35042
8.0%
F 35042
8.0%
C 30283
6.9%
0 27725
6.4%
4 26056
 
6.0%
2 18603
 
4.3%
Other values (5) 44565
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 435992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 65325
15.0%
- 65325
15.0%
3 46944
10.8%
1 41082
9.4%
G 35042
8.0%
F 35042
8.0%
C 30283
6.9%
0 27725
6.4%
4 26056
 
6.0%
2 18603
 
4.3%
Other values (5) 44565
10.2%
Distinct107
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:08.333410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.991543
Min length10

Characters and Unicode

Total characters730388
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-1-179-810
2nd row1-1-193-825
3rd row1-1-193-829
4th row1-1-193-820
5th row1-1-193-819
ValueCountFrequency (%)
2-39-139-28 1805
 
2.7%
1-1-178-823 1496
 
2.3%
2-39-131-30 1411
 
2.1%
1-1-178-828 1313
 
2.0%
1-1-193-819 1251
 
1.9%
1-1-193-820 1192
 
1.8%
1-1-194-821 1170
 
1.8%
2-39-129-17 1140
 
1.7%
2-39-81-4550 1125
 
1.7%
1-1-193-827 1111
 
1.7%
Other values (97) 53436
80.4%
2024-11-14T19:43:08.535269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 199350
27.3%
1 146746
20.1%
9 89636
12.3%
3 65730
 
9.0%
2 64813
 
8.9%
8 55432
 
7.6%
7 52951
 
7.2%
4 15788
 
2.2%
0 15695
 
2.1%
6 12755
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 730388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 199350
27.3%
1 146746
20.1%
9 89636
12.3%
3 65730
 
9.0%
2 64813
 
8.9%
8 55432
 
7.6%
7 52951
 
7.2%
4 15788
 
2.2%
0 15695
 
2.1%
6 12755
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 730388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 199350
27.3%
1 146746
20.1%
9 89636
12.3%
3 65730
 
9.0%
2 64813
 
8.9%
8 55432
 
7.6%
7 52951
 
7.2%
4 15788
 
2.2%
0 15695
 
2.1%
6 12755
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 730388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 199350
27.3%
1 146746
20.1%
9 89636
12.3%
3 65730
 
9.0%
2 64813
 
8.9%
8 55432
 
7.6%
7 52951
 
7.2%
4 15788
 
2.2%
0 15695
 
2.1%
6 12755
 
1.7%

timezone
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
America/Los_Angeles
66450 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1262550
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmerica/Los_Angeles
2nd rowAmerica/Los_Angeles
3rd rowAmerica/Los_Angeles
4th rowAmerica/Los_Angeles
5th rowAmerica/Los_Angeles

Common Values

ValueCountFrequency (%)
America/Los_Angeles 66450
100.0%

Length

2024-11-14T19:43:08.598276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-14T19:43:08.628815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
america/los_angeles 66450
100.0%

Most occurring characters

ValueCountFrequency (%)
e 199350
15.8%
A 132900
 
10.5%
s 132900
 
10.5%
m 66450
 
5.3%
r 66450
 
5.3%
i 66450
 
5.3%
c 66450
 
5.3%
a 66450
 
5.3%
/ 66450
 
5.3%
L 66450
 
5.3%
Other values (5) 332250
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1262550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 199350
15.8%
A 132900
 
10.5%
s 132900
 
10.5%
m 66450
 
5.3%
r 66450
 
5.3%
i 66450
 
5.3%
c 66450
 
5.3%
a 66450
 
5.3%
/ 66450
 
5.3%
L 66450
 
5.3%
Other values (5) 332250
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1262550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 199350
15.8%
A 132900
 
10.5%
s 132900
 
10.5%
m 66450
 
5.3%
r 66450
 
5.3%
i 66450
 
5.3%
c 66450
 
5.3%
a 66450
 
5.3%
/ 66450
 
5.3%
L 66450
 
5.3%
Other values (5) 332250
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1262550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 199350
15.8%
A 132900
 
10.5%
s 132900
 
10.5%
m 66450
 
5.3%
r 66450
 
5.3%
i 66450
 
5.3%
c 66450
 
5.3%
a 66450
 
5.3%
/ 66450
 
5.3%
L 66450
 
5.3%
Other values (5) 332250
26.3%

userID
Real number (ℝ)

Missing 

Distinct1006
Distinct (%)2.0%
Missing17263
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean1800.4946
Minimum1
Maximum19923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:08.664985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile280.6
Q1431
median818
Q31805
95-th percentile7705
Maximum19923
Range19922
Interquartile range (IQR)1374

Descriptive statistics

Standard deviation2617.5486
Coefficient of variation (CV)1.4537942
Kurtosis11.027097
Mean1800.4946
Median Absolute Deviation (MAD)439
Skewness3.1331269
Sum88560927
Variance6851560.7
MonotonicityNot monotonic
2024-11-14T19:43:08.714073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169 545
 
0.8%
651 510
 
0.8%
405 507
 
0.8%
933 507
 
0.8%
743 485
 
0.7%
562 385
 
0.6%
1082 382
 
0.6%
3687 382
 
0.6%
368 353
 
0.5%
420 347
 
0.5%
Other values (996) 44784
67.4%
(Missing) 17263
 
26.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
17 1
 
< 0.1%
22 58
0.1%
43 1
 
< 0.1%
45 1
 
< 0.1%
58 5
 
< 0.1%
61 43
0.1%
65 5
 
< 0.1%
66 44
0.1%
67 61
0.1%
ValueCountFrequency (%)
19923 1
 
< 0.1%
19869 1
 
< 0.1%
19652 1
 
< 0.1%
19548 3
< 0.1%
19273 3
< 0.1%
19203 1
 
< 0.1%
19139 4
< 0.1%
19055 2
< 0.1%
19017 1
 
< 0.1%
18908 1
 
< 0.1%

WhPerMile
Real number (ℝ)

Missing 

Distinct178
Distinct (%)0.4%
Missing17263
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean361.44416
Minimum50
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:08.761851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile250
Q1288
median354
Q3400
95-th percentile600
Maximum2000
Range1950
Interquartile range (IQR)112

Descriptive statistics

Standard deviation101.89952
Coefficient of variation (CV)0.28192328
Kurtosis33.240202
Mean361.44416
Median Absolute Deviation (MAD)46
Skewness2.8196656
Sum17778354
Variance10383.513
MonotonicityNot monotonic
2024-11-14T19:43:08.811758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 15303
23.0%
250 4610
 
6.9%
600 3126
 
4.7%
300 2575
 
3.9%
333 2271
 
3.4%
350 1966
 
3.0%
310 1169
 
1.8%
200 1134
 
1.7%
500 1075
 
1.6%
375 809
 
1.2%
Other values (168) 15149
22.8%
(Missing) 17263
26.0%
ValueCountFrequency (%)
50 1
 
< 0.1%
72 3
 
< 0.1%
80 21
 
< 0.1%
120 1
 
< 0.1%
200 1134
1.7%
205 2
 
< 0.1%
207 9
 
< 0.1%
208 10
 
< 0.1%
211 7
 
< 0.1%
213 1
 
< 0.1%
ValueCountFrequency (%)
2000 22
 
< 0.1%
1538 10
 
< 0.1%
769 15
 
< 0.1%
630 52
 
0.1%
600 3126
4.7%
593 1
 
< 0.1%
590 29
 
< 0.1%
583 9
 
< 0.1%
577 3
 
< 0.1%
575 76
 
0.1%

kWhRequested
Real number (ℝ)

High correlation  Missing 

Distinct1201
Distinct (%)2.4%
Missing17263
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean25.136324
Minimum0
Maximum215.32
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:08.859308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.66
Q112
median19
Q332
95-th percentile65.1
Maximum215.32
Range215.32
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.788129
Coefficient of variation (CV)0.82701547
Kurtosis11.0455
Mean25.136324
Median Absolute Deviation (MAD)9
Skewness2.5700504
Sum1236380.4
Variance432.14629
MonotonicityNot monotonic
2024-11-14T19:43:08.904778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 3867
 
5.8%
20 3186
 
4.8%
12 3073
 
4.6%
16 2614
 
3.9%
40 1536
 
2.3%
24 1496
 
2.3%
10 1152
 
1.7%
15 1136
 
1.7%
30 815
 
1.2%
18 700
 
1.1%
Other values (1191) 29612
44.6%
(Missing) 17263
26.0%
ValueCountFrequency (%)
0 17
< 0.1%
0.256 1
 
< 0.1%
1.9 2
 
< 0.1%
2 3
 
< 0.1%
2.25 2
 
< 0.1%
2.27 2
 
< 0.1%
2.5 37
0.1%
2.59 1
 
< 0.1%
2.67 2
 
< 0.1%
2.73 2
 
< 0.1%
ValueCountFrequency (%)
215.32 10
 
< 0.1%
200 22
 
< 0.1%
199.95 8
 
< 0.1%
180 30
< 0.1%
156 1
 
< 0.1%
153.8 11
 
< 0.1%
150 61
0.1%
149.93 2
 
< 0.1%
144.76 3
 
< 0.1%
144 1
 
< 0.1%

milesRequested
Real number (ℝ)

High correlation  Missing 

Distinct106
Distinct (%)0.2%
Missing17263
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean74.499278
Minimum0
Maximum775
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:08.950231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q130
median50
Q3100
95-th percentile200
Maximum775
Range775
Interquartile range (IQR)70

Descriptive statistics

Standard deviation62.217567
Coefficient of variation (CV)0.83514321
Kurtosis5.1274858
Mean74.499278
Median Absolute Deviation (MAD)30
Skewness1.8281768
Sum3664396
Variance3871.0256
MonotonicityNot monotonic
2024-11-14T19:43:08.999384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 8366
12.6%
40 6073
 
9.1%
50 5790
 
8.7%
30 4686
 
7.1%
100 4658
 
7.0%
60 3147
 
4.7%
80 2244
 
3.4%
70 1910
 
2.9%
200 1358
 
2.0%
90 1128
 
1.7%
Other values (96) 9827
14.8%
(Missing) 17263
26.0%
ValueCountFrequency (%)
0 17
 
< 0.1%
1 1
 
< 0.1%
5 2
 
< 0.1%
10 607
 
0.9%
12 1
 
< 0.1%
15 53
 
0.1%
17 9
 
< 0.1%
19 27
 
< 0.1%
20 8366
12.6%
21 70
 
0.1%
ValueCountFrequency (%)
775 8
 
< 0.1%
480 1
 
< 0.1%
422 44
 
0.1%
390 2
 
< 0.1%
330 1
 
< 0.1%
320 79
 
0.1%
310 13
 
< 0.1%
305 3
 
< 0.1%
300 285
0.4%
290 112
 
0.2%

minutesAvailable
Real number (ℝ)

Missing 

Distinct842
Distinct (%)1.7%
Missing17263
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean358.35959
Minimum1
Maximum10062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.3 KiB
2024-11-14T19:43:09.048004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q1193
median360
Q3501
95-th percentile639
Maximum10062
Range10061
Interquartile range (IQR)308

Descriptive statistics

Standard deviation196.1254
Coefficient of variation (CV)0.5472866
Kurtosis122.15961
Mean358.35959
Median Absolute Deviation (MAD)155
Skewness2.8028156
Sum17626633
Variance38465.173
MonotonicityNot monotonic
2024-11-14T19:43:09.094430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 3041
 
4.6%
60 1831
 
2.8%
720 773
 
1.2%
120 726
 
1.1%
240 626
 
0.9%
360 616
 
0.9%
300 550
 
0.8%
180 483
 
0.7%
576 346
 
0.5%
288 322
 
0.5%
Other values (832) 39873
60.0%
(Missing) 17263
26.0%
ValueCountFrequency (%)
1 3
 
< 0.1%
4 2
 
< 0.1%
5 13
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
10062 1
 
< 0.1%
1913 1
 
< 0.1%
1621 1
 
< 0.1%
1550 1
 
< 0.1%
1472 1
 
< 0.1%
1467 13
< 0.1%
1449 1
 
< 0.1%
1448 8
 
< 0.1%
1442 32
< 0.1%
1428 8
 
< 0.1%

modifiedAt
Date

Missing 

Distinct47704
Distinct (%)97.0%
Missing17263
Missing (%)26.0%
Memory size519.3 KiB
Minimum2018-04-30 08:08:54-07:00
Maximum2021-09-13 22:43:38-07:00
2024-11-14T19:43:09.140015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:09.189887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

paymentRequired
Boolean

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing17263
Missing (%)26.0%
Memory size519.3 KiB
True
49187 
(Missing)
17263 
ValueCountFrequency (%)
True 49187
74.0%
(Missing) 17263
 
26.0%
2024-11-14T19:43:09.224100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

requestedDeparture
Date

Missing 

Distinct47773
Distinct (%)97.1%
Missing17263
Missing (%)26.0%
Memory size519.3 KiB
Minimum2018-04-30 17:17:49-07:00
Maximum2021-09-14 10:15:39-07:00
2024-11-14T19:43:09.259731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:09.308596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

connectionTimespan
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size519.3 KiB

connectionMonth
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6308352
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size259.7 KiB
2024-11-14T19:43:09.347743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3182489
Coefficient of variation (CV)0.50042698
Kurtosis-1.0902495
Mean6.6308352
Median Absolute Deviation (MAD)3
Skewness-0.13455148
Sum440619
Variance11.010775
MonotonicityNot monotonic
2024-11-14T19:43:09.384027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 7362
11.1%
10 6582
9.9%
8 6457
9.7%
9 6123
9.2%
6 5769
8.7%
5 5658
8.5%
1 5339
8.0%
2 5218
7.9%
11 4925
7.4%
12 4508
6.8%
Other values (2) 8509
12.8%
ValueCountFrequency (%)
1 5339
8.0%
2 5218
7.9%
3 4484
6.7%
4 4025
6.1%
5 5658
8.5%
6 5769
8.7%
7 7362
11.1%
8 6457
9.7%
9 6123
9.2%
10 6582
9.9%
ValueCountFrequency (%)
12 4508
6.8%
11 4925
7.4%
10 6582
9.9%
9 6123
9.2%
8 6457
9.7%
7 7362
11.1%
6 5769
8.7%
5 5658
8.5%
4 4025
6.1%
3 4484
6.7%

connectionWeekdayName
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.3 KiB
Tuesday
12969 
Wednesday
12767 
Thursday
12505 
Monday
12067 
Friday
10421 
Other values (2)
5721 

Length

Max length9
Median length8
Mean length7.2385553
Min length6

Characters and Unicode

Total characters481002
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Tuesday 12969
19.5%
Wednesday 12767
19.2%
Thursday 12505
18.8%
Monday 12067
18.2%
Friday 10421
15.7%
Saturday 3011
 
4.5%
Sunday 2710
 
4.1%

Length

2024-11-14T19:43:09.424671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-14T19:43:09.464045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 12969
19.5%
wednesday 12767
19.2%
thursday 12505
18.8%
monday 12067
18.2%
friday 10421
15.7%
saturday 3011
 
4.5%
sunday 2710
 
4.1%

Most occurring characters

ValueCountFrequency (%)
d 79217
16.5%
a 69461
14.4%
y 66450
13.8%
e 38503
8.0%
s 38241
8.0%
u 31195
 
6.5%
n 27544
 
5.7%
r 25937
 
5.4%
T 25474
 
5.3%
W 12767
 
2.7%
Other values (7) 66213
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 481002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 79217
16.5%
a 69461
14.4%
y 66450
13.8%
e 38503
8.0%
s 38241
8.0%
u 31195
 
6.5%
n 27544
 
5.7%
r 25937
 
5.4%
T 25474
 
5.3%
W 12767
 
2.7%
Other values (7) 66213
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 481002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 79217
16.5%
a 69461
14.4%
y 66450
13.8%
e 38503
8.0%
s 38241
8.0%
u 31195
 
6.5%
n 27544
 
5.7%
r 25937
 
5.4%
T 25474
 
5.3%
W 12767
 
2.7%
Other values (7) 66213
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 481002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 79217
16.5%
a 69461
14.4%
y 66450
13.8%
e 38503
8.0%
s 38241
8.0%
u 31195
 
6.5%
n 27544
 
5.7%
r 25937
 
5.4%
T 25474
 
5.3%
W 12767
 
2.7%
Other values (7) 66213
13.8%

isWeekend
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.0 KiB
False
60729 
True
 
5721
ValueCountFrequency (%)
False 60729
91.4%
True 5721
 
8.6%
2024-11-14T19:43:09.502255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-11-14T19:43:05.911028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.198001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.457964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.743787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.136560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.385847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.654914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.947036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.234711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.496980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.783292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.171426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.422588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.689532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.987094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.273326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.540835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.827349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.209500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.463318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.730237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:06.028963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.311475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.581881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.871130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.247257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.503593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.768151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:06.065491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.345854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.617938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.908187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.279613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.538174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.802883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:06.107374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.384097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.662943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.951582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.316295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.578086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.841349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:06.143774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.421321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.703084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:04.991098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.351460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.616265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-14T19:43:05.875999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-14T19:43:09.660233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
WhPerMileconnectionMonthconnectionWeekdayNameisWeekendkWhDeliveredkWhRequestedmilesRequestedminutesAvailablesiteIDuserID
WhPerMile1.0000.0050.0180.026-0.204-0.109-0.398-0.1250.063-0.049
connectionMonth0.0051.0000.0390.058-0.031-0.028-0.029-0.0150.166-0.062
connectionWeekdayName0.0180.0391.0001.0000.0230.0230.0350.0000.2320.035
isWeekend0.0260.0581.0001.0000.0140.0440.0710.0050.2260.080
kWhDelivered-0.204-0.0310.0230.0141.0000.6910.6980.3890.2080.044
kWhRequested-0.109-0.0280.0230.0440.6911.0000.9450.4200.1060.081
milesRequested-0.398-0.0290.0350.0710.6980.9451.0000.4140.0880.086
minutesAvailable-0.125-0.0150.0000.0050.3890.4200.4141.0000.009-0.009
siteID0.0630.1660.2320.2260.2080.1060.0880.0091.0000.182
userID-0.049-0.0620.0350.0800.0440.0810.086-0.0090.1821.000

Missing values

2024-11-14T19:43:06.208266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-14T19:43:06.362302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-14T19:43:06.563068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idconnectionTimedisconnectTimedoneChargingTimekWhDeliveredsessionIDsiteIDspaceIDstationIDtimezoneuserIDWhPerMilekWhRequestedmilesRequestedminutesAvailablemodifiedAtpaymentRequiredrequestedDepartureconnectionTimespanconnectionMonthconnectionWeekdayNameisWeekend
05e23b149f9af8b5fe4b973cf2020-01-02 05:08:54-08:002020-01-02 11:11:15-08:002020-01-02 09:31:35-08:0025.0161_1_179_810_2020-01-02 13:08:53.8700341AG-3F301-1-179-810America/Los_Angeles194.0250.025.00100.0463.02020-01-02 05:09:39-08:00True2020-01-02 12:51:54-08:000 days 06:02:211ThursdayFalse
15e23b149f9af8b5fe4b973d02020-01-02 05:36:50-08:002020-01-02 14:38:21-08:002020-01-02 12:18:05-08:0033.0971_1_193_825_2020-01-02 13:36:49.5998531AG-1F011-1-193-825America/Los_Angeles4275.0280.070.00250.0595.02020-01-02 05:37:11-08:00True2020-01-02 15:31:50-08:000 days 09:01:311ThursdayFalse
25e23b149f9af8b5fe4b973d12020-01-02 05:56:35-08:002020-01-02 16:39:22-08:002020-01-02 08:35:06-08:006.5211_1_193_829_2020-01-02 13:56:35.2149931AG-1F031-1-193-829America/Los_Angeles344.0400.08.0020.060.02020-01-02 05:57:17-08:00True2020-01-02 06:56:35-08:000 days 10:42:471ThursdayFalse
35e23b149f9af8b5fe4b973d22020-01-02 05:59:58-08:002020-01-02 08:38:39-08:002020-01-02 07:18:45-08:002.3551_1_193_820_2020-01-02 13:59:58.3093191AG-1F041-1-193-820America/Los_Angeles1117.0400.08.0020.065.02020-01-02 06:00:03-08:00True2020-01-02 07:04:58-08:000 days 02:38:411ThursdayFalse
45e23b149f9af8b5fe4b973d32020-01-02 06:00:01-08:002020-01-02 14:08:40-08:002020-01-02 10:17:30-08:0013.3751_1_193_819_2020-01-02 14:00:00.7799671AG-1F061-1-193-819America/Los_Angeles334.0400.016.0040.0504.02020-01-02 06:00:13-08:00True2020-01-02 14:24:01-08:000 days 08:08:391ThursdayFalse
55e23b149f9af8b5fe4b973d42020-01-02 06:00:13-08:002020-01-02 15:00:41-08:002020-01-02 12:13:21-08:0013.3701_1_194_821_2020-01-02 14:00:05.2415731AG-1F131-1-194-821America/Los_Angeles3519.0600.024.0040.0624.02020-01-02 06:00:12-08:00True2020-01-02 16:24:13-08:000 days 09:00:281ThursdayFalse
65e23b149f9af8b5fe4b973d52020-01-02 06:09:14-08:002020-01-02 17:57:58-08:002020-01-02 16:59:21-08:0043.4771_1_178_817_2020-01-02 14:09:14.4518271AG-1F091-1-178-817America/Los_Angeles933.0385.065.45170.0647.02020-01-02 06:09:33-08:00True2020-01-02 16:56:14-08:000 days 11:48:441ThursdayFalse
75e23b149f9af8b5fe4b973d62020-01-02 06:17:32-08:002020-01-02 16:44:54-08:002020-01-02 12:22:40-08:0032.4411_1_191_804_2020-01-02 14:17:32.3295351AG-4F341-1-191-804America/Los_Angeles3737.0300.045.00150.0126.02020-01-02 06:17:40-08:00True2020-01-02 08:23:32-08:000 days 10:27:221ThursdayFalse
85e23b149f9af8b5fe4b973d72020-01-02 06:25:38-08:002020-01-02 11:36:05-08:002020-01-02 10:38:39-08:0013.2641_1_194_826_2020-01-02 14:25:37.5786921AG-1F111-1-194-826America/Los_Angeles419.0400.040.00100.0491.02020-01-02 06:25:45-08:00True2020-01-02 14:36:38-08:000 days 05:10:271ThursdayFalse
95e23b149f9af8b5fe4b973d82020-01-02 06:27:40-08:002020-01-02 12:01:48-08:002020-01-02 11:17:46-08:0013.2851_1_178_823_2020-01-02 14:27:39.5823371AG-1F081-1-178-823America/Los_Angeles651.0400.020.0050.0226.02020-01-02 06:27:48-08:00True2020-01-02 10:13:40-08:000 days 05:34:081ThursdayFalse
idconnectionTimedisconnectTimedoneChargingTimekWhDeliveredsessionIDsiteIDspaceIDstationIDtimezoneuserIDWhPerMilekWhRequestedmilesRequestedminutesAvailablemodifiedAtpaymentRequiredrequestedDepartureconnectionTimespanconnectionMonthconnectionWeekdayNameisWeekend
664405d574ad2f9af8b4c10c0364d2019-07-31 07:45:29-07:002019-07-31 15:01:32-07:002019-07-31 15:02:32-07:0031.3761_1_191_811_2019-07-31 14:45:29.3880461AG-4F421-1-191-811America/Los_Angeles1626.0200.038.00190.0209.02019-07-31 07:45:50-07:00True2019-07-31 11:14:29-07:000 days 07:16:037WednesdayFalse
664415d574ad2f9af8b4c10c0364e2019-07-31 07:46:58-07:002019-07-31 17:40:07-07:002019-07-31 10:22:58-07:005.0511_1_191_782_2019-07-31 14:46:58.3268971AG-4F501-1-191-782America/Los_Angeles2883.0375.07.5020.0163.02019-07-31 07:47:05-07:00True2019-07-31 10:29:58-07:000 days 09:53:097WednesdayFalse
664425d574ad2f9af8b4c10c0364f2019-07-31 07:48:11-07:002019-07-31 18:33:50-07:002019-07-31 17:42:45-07:009.5461_1_191_793_2019-07-31 14:48:10.5686621AG-4F381-1-191-793America/Los_Angeles410.0600.048.0080.0287.02019-07-31 07:48:33-07:00True2019-07-31 12:35:11-07:000 days 10:45:397WednesdayFalse
664435d574ad2f9af8b4c10c036502019-07-31 07:50:02-07:002019-07-31 16:19:49-07:002019-07-31 10:11:26-07:009.0831_1_191_786_2019-07-31 14:50:01.7049441AG-4F361-1-191-786America/Los_Angeles607.0364.032.7690.0533.02019-07-31 07:50:13-07:00True2019-07-31 16:43:02-07:000 days 08:29:477WednesdayFalse
664445d574ad2f9af8b4c10c036512019-07-31 07:50:17-07:002019-07-31 18:01:18-07:002019-07-31 13:29:24-07:0027.1741_1_191_784_2019-07-31 14:50:17.0373671AG-4F401-1-191-784America/Los_Angeles448.0200.028.00140.060.02019-07-31 07:53:40-07:00True2019-07-31 08:50:17-07:000 days 10:11:017WednesdayFalse
664455d574ad2f9af8b4c10c036522019-07-31 11:08:04-07:002019-07-31 16:29:18-07:002019-07-31 16:30:18-07:0028.7871_1_179_809_2019-07-31 18:08:04.4326541AG-3F271-1-179-809America/Los_Angeles393.0240.031.20130.0355.02019-07-31 11:08:23-07:00True2019-07-31 17:03:04-07:000 days 05:21:147WednesdayFalse
664465d574ad2f9af8b4c10c036532019-07-31 11:40:41-07:002019-07-31 17:59:42-07:002019-07-31 14:44:23-07:007.7871_1_179_810_2019-07-31 18:40:40.9002031AG-3F301-1-179-810America/Los_Angeles220.0333.06.6620.0455.02019-07-31 11:41:02-07:00True2019-07-31 19:15:41-07:000 days 06:19:017WednesdayFalse
664475d574ad2f9af8b4c10c036542019-07-31 12:04:40-07:002019-07-31 15:44:22-07:002019-07-31 15:45:21-07:0011.2741_1_191_795_2019-07-31 19:04:40.0982731AG-4F511-1-191-795America/Los_Angeles1974.0333.019.9860.0184.02019-07-31 12:04:57-07:00True2019-07-31 15:08:40-07:000 days 03:39:427WednesdayFalse
664485d574ad2f9af8b4c10c036552019-07-31 12:19:47-07:002019-07-31 17:34:51-07:002019-07-31 14:25:30-07:0011.5891_1_191_778_2019-07-31 19:19:46.9193581AG-4F431-1-191-778America/Los_Angeles942.0275.022.0080.060.02019-07-31 12:20:10-07:00True2019-07-31 13:19:47-07:000 days 05:15:047WednesdayFalse
664495d574ad2f9af8b4c10c036562019-07-31 12:21:47-07:002019-07-31 15:00:04-07:002019-07-31 13:51:34-07:000.8971_1_178_817_2019-07-31 19:21:46.7276971AG-1F091-1-178-817America/Los_AngelesNaNNaNNaNNaNNaNNaTNaNNaT0 days 02:38:177WednesdayFalse

Duplicate rows

Most frequently occurring

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05d2fbdd3f9af8b4d0dd0d54f2019-07-01 17:32:46-07:002019-07-01 19:34:56-07:002019-07-01 18:46:09-07:002.4631_1_193_827_2019-07-02 00:32:45.8200791AG-1F021-1-193-827America/Los_Angeles1117.0200.04.0020.090.02019-07-01 17:32:53-07:00True2019-07-01 19:02:46-07:007MondayFalse2
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25d310f54f9af8b52adda3e212019-07-02 05:42:49-07:002019-07-02 12:34:19-07:002019-07-02 09:21:21-07:0013.7341_1_178_828_2019-07-02 12:42:48.7334641AG-1F101-1-178-828America/Los_Angeles651.0400.020.0050.0186.02019-07-02 05:43:19-07:00True2019-07-02 08:48:49-07:007TuesdayFalse2
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85d310f54f9af8b52adda3e272019-07-02 06:18:28-07:002019-07-02 16:16:24-07:002019-07-02 15:45:11-07:0018.3681_1_179_788_2019-07-02 13:18:27.8246191AG-3F221-1-179-788America/Los_Angeles529.0300.018.0060.0541.02019-07-02 06:18:44-07:00True2019-07-02 15:19:28-07:007TuesdayFalse2
95d310f54f9af8b52adda3e282019-07-02 06:25:01-07:002019-07-02 15:39:18-07:002019-07-02 09:29:23-07:0013.9241_1_178_817_2019-07-02 13:25:01.4646541AG-1F091-1-178-817America/Los_Angeles322.0313.018.7860.0418.02019-07-02 06:25:07-07:00True2019-07-02 13:23:01-07:007TuesdayFalse2